The contradiction of variable step size least mean square(LMS)algorithm between fast convergence speed and small steady-state error has always existed.So,a new algorithm based on the combination of logarithmic and sym...The contradiction of variable step size least mean square(LMS)algorithm between fast convergence speed and small steady-state error has always existed.So,a new algorithm based on the combination of logarithmic and symbolic function and step size factor is proposed.It establishes a new updating method of step factor that is related to step factor and error signal.This work makes an analysis from 3 aspects:theoretical analysis,theoretical verification and specific experiments.The experimental results show that the proposed algorithm is superior to other variable step size algorithms in convergence speed and steady-state error.展开更多
This paper puts forward a new variable step size LMS adaptive algorithm based on variable region. The step size p（k） in the algorithm varies with the variation of the region of deviation e （k） to ensure the optimi...This paper puts forward a new variable step size LMS adaptive algorithm based on variable region. The step size p（k） in the algorithm varies with the variation of the region of deviation e （k） to ensure the optimization of the three performance objectives including initial convergent speed, trace ability of the time-varying system and steady disregulation. The paper demonstrates the convergence of the algorithm accompanied by random noise,展开更多
论文研究了自适应最小均方误差(Least Mean Squares,LMS)滤波算法的步长选取问题。在分析现有算法的基础上,通过构造步长与误差信号之间的非线性函数,提出一种新的变步长LMS算法。新算法采用误差信号的自相关估计值控制步长,而不是直接...论文研究了自适应最小均方误差(Least Mean Squares,LMS)滤波算法的步长选取问题。在分析现有算法的基础上,通过构造步长与误差信号之间的非线性函数,提出一种新的变步长LMS算法。新算法采用误差信号的自相关估计值控制步长,而不是直接利用瞬时误差控制步长,避免了噪声干扰,降低了稳态失调,可工作于低信噪比环境。同时新算法步长控制无记忆效应,提高了收敛速度。仿真表明,新算法的稳态失调和收敛速度均优于现有变步长LMS算法。展开更多
为解决自适应最小均方误差(least mean squares,LMS)滤波算法难以平衡稳态误差和收敛速度的问题,提出了基于对称非线性函数的变步长LMS自适应滤波算法。通过自变量取绝对值、叠加非线性拉伸量改进Sig-moid函数,构造一个对称非线性函数...为解决自适应最小均方误差(least mean squares,LMS)滤波算法难以平衡稳态误差和收敛速度的问题,提出了基于对称非线性函数的变步长LMS自适应滤波算法。通过自变量取绝对值、叠加非线性拉伸量改进Sig-moid函数,构造一个对称非线性函数用于刻画步长因子与稳态误差的非线性关系。该对称非线性函数具有能够根据误差动态调整步长、更快达到收敛状态的特点。根据构造的对称非线性函数和输入信号功率生成归一化变步长因子,解决噪声逐级放大的问题,进一步提高算法的滤波效果同时,加速收敛。实验表明:该算法在低信噪比、信噪比变化、信号频率变化、滤波器阶数变化、延迟采样点数变化条件下均具有更好的滤波效果、更优的稳定性和更快的收敛速度。展开更多
To solve the contradiction between convergence rate and steady-state error in least mean square (LMS) algorithm, basing on independence assumption, this paper proposes and proves the optimal step-size theorem from the...To solve the contradiction between convergence rate and steady-state error in least mean square (LMS) algorithm, basing on independence assumption, this paper proposes and proves the optimal step-size theorem from the view of minimizing mean squared error (MSE). The theorem reveals the one-to-one mapping between the optimal step-size and MSE. Following the theorem, optimal variable step-size LMS (OVS-LMS) model, describing the theoretical bound of the convergence rate of LMS algorithm, is constructed. Then we discuss the selection of initial optimal step-size and updating of optimal step-size at the time of unknown system changing. At last an optimal step-size LMS algorithm is proposed and tested in various environments. Simulation results show the proposed algorithm is very close to the theoretical bound.展开更多
为了改进现有的变步长最小均方误差(least mean square,LMS)算法在低信噪比时性能较差的缺陷,提出了一种基于改进的双曲正切函数的变步长LMS算法,从理论分析和仿真实验两方面讨论了引入参数对算法收敛性、跟踪性、稳定性的影响及算法的...为了改进现有的变步长最小均方误差(least mean square,LMS)算法在低信噪比时性能较差的缺陷,提出了一种基于改进的双曲正切函数的变步长LMS算法,从理论分析和仿真实验两方面讨论了引入参数对算法收敛性、跟踪性、稳定性的影响及算法的抗干扰性。理论分析和仿真实验表明该算法在高低信噪比时均具有较快的收敛速度和跟踪速度以及较小的稳态误差和稳态失调,并且在低信噪比时该算法的收敛性、跟踪性、稳态性均优于其他多种变步长算法。展开更多
针对固定步长LMS(Least Mean Square)算法(FXSSLMS)不能同时满足快速收敛和小稳态失调误差的问题,该文提出了迭代变步长LMS算法(IVSSLMS)。与已有的变步长LMS算法(VSSLMS)不同,该算法的步长因子不再是由输出误差信号控制,而是建立了与...针对固定步长LMS(Least Mean Square)算法(FXSSLMS)不能同时满足快速收敛和小稳态失调误差的问题,该文提出了迭代变步长LMS算法(IVSSLMS)。与已有的变步长LMS算法(VSSLMS)不同,该算法的步长因子不再是由输出误差信号控制,而是建立了与迭代时间的改进Logistic函数非线性关系,克服了定步长算法收敛慢及已有变步长算法抗噪声干扰能力差的问题。最后从理论上分析了算法的性能,给出了其参数取值方法。理论分析和仿真均表明,所提算法能够在快速收敛情况下获得小的稳态失调误差,在有色噪声干扰下稳态失调误差比已有算法降低了约7 d B。展开更多
基金the National Natural Science Foundation of China(No.51575328,61503232).
文摘The contradiction of variable step size least mean square(LMS)algorithm between fast convergence speed and small steady-state error has always existed.So,a new algorithm based on the combination of logarithmic and symbolic function and step size factor is proposed.It establishes a new updating method of step factor that is related to step factor and error signal.This work makes an analysis from 3 aspects:theoretical analysis,theoretical verification and specific experiments.The experimental results show that the proposed algorithm is superior to other variable step size algorithms in convergence speed and steady-state error.
基金Supported by Natural Science Foundation of Beijing of China (No.2005AA501140)
文摘This paper puts forward a new variable step size LMS adaptive algorithm based on variable region. The step size p（k） in the algorithm varies with the variation of the region of deviation e （k） to ensure the optimization of the three performance objectives including initial convergent speed, trace ability of the time-varying system and steady disregulation. The paper demonstrates the convergence of the algorithm accompanied by random noise,
文摘论文研究了自适应最小均方误差(Least Mean Squares,LMS)滤波算法的步长选取问题。在分析现有算法的基础上,通过构造步长与误差信号之间的非线性函数,提出一种新的变步长LMS算法。新算法采用误差信号的自相关估计值控制步长,而不是直接利用瞬时误差控制步长,避免了噪声干扰,降低了稳态失调,可工作于低信噪比环境。同时新算法步长控制无记忆效应,提高了收敛速度。仿真表明,新算法的稳态失调和收敛速度均优于现有变步长LMS算法。
文摘为解决自适应最小均方误差(least mean squares,LMS)滤波算法难以平衡稳态误差和收敛速度的问题,提出了基于对称非线性函数的变步长LMS自适应滤波算法。通过自变量取绝对值、叠加非线性拉伸量改进Sig-moid函数,构造一个对称非线性函数用于刻画步长因子与稳态误差的非线性关系。该对称非线性函数具有能够根据误差动态调整步长、更快达到收敛状态的特点。根据构造的对称非线性函数和输入信号功率生成归一化变步长因子,解决噪声逐级放大的问题,进一步提高算法的滤波效果同时,加速收敛。实验表明:该算法在低信噪比、信噪比变化、信号频率变化、滤波器阶数变化、延迟采样点数变化条件下均具有更好的滤波效果、更优的稳定性和更快的收敛速度。
基金This work was supported in part by the National Fundamental Research Program(Grant No.G1998030406)the National Natural Science Foundation of China(Grant No.69972020)by the State Key Lab on Microwave and Digital Communications,Department of Electronics Engineering,Tsinghua University.
文摘To solve the contradiction between convergence rate and steady-state error in least mean square (LMS) algorithm, basing on independence assumption, this paper proposes and proves the optimal step-size theorem from the view of minimizing mean squared error (MSE). The theorem reveals the one-to-one mapping between the optimal step-size and MSE. Following the theorem, optimal variable step-size LMS (OVS-LMS) model, describing the theoretical bound of the convergence rate of LMS algorithm, is constructed. Then we discuss the selection of initial optimal step-size and updating of optimal step-size at the time of unknown system changing. At last an optimal step-size LMS algorithm is proposed and tested in various environments. Simulation results show the proposed algorithm is very close to the theoretical bound.
文摘为了改进现有的变步长最小均方误差(least mean square,LMS)算法在低信噪比时性能较差的缺陷,提出了一种基于改进的双曲正切函数的变步长LMS算法,从理论分析和仿真实验两方面讨论了引入参数对算法收敛性、跟踪性、稳定性的影响及算法的抗干扰性。理论分析和仿真实验表明该算法在高低信噪比时均具有较快的收敛速度和跟踪速度以及较小的稳态误差和稳态失调,并且在低信噪比时该算法的收敛性、跟踪性、稳态性均优于其他多种变步长算法。
文摘针对固定步长LMS(Least Mean Square)算法(FXSSLMS)不能同时满足快速收敛和小稳态失调误差的问题,该文提出了迭代变步长LMS算法(IVSSLMS)。与已有的变步长LMS算法(VSSLMS)不同,该算法的步长因子不再是由输出误差信号控制,而是建立了与迭代时间的改进Logistic函数非线性关系,克服了定步长算法收敛慢及已有变步长算法抗噪声干扰能力差的问题。最后从理论上分析了算法的性能,给出了其参数取值方法。理论分析和仿真均表明,所提算法能够在快速收敛情况下获得小的稳态失调误差,在有色噪声干扰下稳态失调误差比已有算法降低了约7 d B。
